NREL Open-Source Modeling Framework Cracks the Code of Simulating Low-Inertia Energy Systems

Analysts Share a Novel Scientific Computing Approach to Large-Scale Energy Systems Modeling

July 20, 2021 | Contact media relations

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Modeling rapidly evolving energy systems with large shares of renewables just got easier with a new open-source tool set from NREL. Photo from iStock

The United States has 37 gigawatts (GW) of utility-scale solar capacity—enough to power over 4,070,000,000 LED lights—with an impressive additional 112 GW of capacity currently under development.

With so much large-scale solar power already in place, current trends in energy systems clearly point to renewable energy sources and battery energy storage systems being major players in the power grids of the future. But these new technologies bring additional complexities and challenges. Given the obstacles, how can we understand the behavior of modernized grids and the ways in which system operators and policymakers can ensure their continued reliability on a large scale? NREL analysts, along with colleagues at the University of California, Berkeley (UCB), have published a novel open-source approach to simulation of modern energy systems in an IEEE Electrification article that is helping unlock the answer.

"Existing commercial software tools used for modeling have worked well for power system analysis for decades. However, we are in a phase of rapid energy system changes that is placing new demands on modeling needs," said Clayton Barrows, NREL senior researcher and contributing author of the article. "In order to keep pace with these emerging technologies we need transparent software that is easy to modify. Updated and flexible software tools will allow the research community to address computational questions and understand the impacts of new technologies before they hit the market."

Understanding Low-Inertia Power Systems

The introduction of renewable energy sources and battery energy storage systems, as well as the move away from traditional rotating generators, has resulted in unfamiliar power systems with low levels of physical inertia. The power systems of the past were dominated by synchronous machines in which a crucial source of grid stability was physical rotations that behaved according to the laws of physics. Modern power systems, however, have renewable energy sources as well as inverter-based generation where stability is maintained not through mechanical processes but through logic and electronic controls.

All of this has fundamentally changed our understanding of grid stability and behavior—and presented fresh obstacles to studying and predicting these systems. The new NREL- and UCB-developed modeling approach addresses the shortfalls created by the changing energy systems of the emerging grid.

Closing the Modeling Gap with Scientific Computing

Computational tools and simulations are uniquely poised to handle the complexity and scale of power system analysis. Scientific computing allows researchers to map and understand power systems containing widespread renewable energy sources and battery energy storage systems. Ideally, computer-aided simulations are replicable, with results that can be validated, and computation models can be scaled to reflect the real-world proportions of our modernized grids.

Scalability and flexibility have previously been the biggest obstacles for researchers in the field. Large-scale experiments have required proprietary models and algorithms that are expensive and time-consuming to set up and are difficult—if not impossible—to fully represent emerging technologies. This inaccessibility ultimately impedes research and innovation in the power systems community, which hinders the deployment of modernized grid systems.

NREL and UCB analysts saw a way to overcome these challenges and have rolled out a set of open-source simulation tools and a computational approach that can close the access gap.

Choosing a Common Language

Developing any simulation tool starts with choosing a programming language. The NREL analysts behind the recent article argue that Julia—a dynamically typed programming language developed by Bezanson et al. 2017—is the best answer for large-scale energy system modeling.

Julia is designed to make high-performance computing more accessible by bridging the gap between scripting languages and high-performance computing languages. Julia makes it easy to write and maintain extremely reliable, well-performing software. And software that is easy to write is also easy to read and reproduce. These capabilities, the NREL analysts determined, make Julia an excellent match to tackle scientific computing challenges in the power systems community.

Establishing an Accessible Modeling Framework

With a programming language decided, the NREL team set out to develop a fully accessible, flexible, and extensible tool set that meets the research needs of ever-evolving modern power systems. The result is Sienna (formerly the Scalable Integrated Infrastructure Planning framework)—a first-of its-kind modular framework that incorporates new solution algorithms, advanced data analytics, and scalable high-performance computing to enable efficient large-scale simulations of modern energy systems.

Julia features and capabilities are being used extensively in Sienna to provide open-source tools that provide consistent and high-performance data models for large-scale energy systems.

The open-source Sienna environment consists of a suite of three interoperable software applications, each focused on a different capability: Sienna\Data, Sienna\Ops, and Sienna\Dyn.

These core capabilities are enabled by various combinations of the seven software packages that form Sienna’s basic building blocks. Each package supports Sienna’s prodigious functionality in specific ways:

  1. PowerSystems.jl provides efficient power systems data specifications (which define the units, relationships, and labels associated with system data values) and supports translation of standard energy systems data file formats as well as basic data transformations and calculations.
  2. PowerSimulations.jl enables simulation of power systems across timescales and complexity (e.g., simplified monthly/seasonal scheduling of hydropower resources to detailed minute-to-minute nonlinear power flow and generator dispatch).
  3. PowerSimulationsDynamics.jl allows for the simulation of energy system dynamics (e.g., simulating the ability of a system to maintain synchronous operation in response to changes in load or generation) geared toward the modeling requirements of systems with large shares of inverter-based resources or renewable generation connected with power electronic inverters.
  4. PowerFlows.jl provides methods for the analysis of power flow through a network of transmission lines (i.e., methods to calculate power flow through every transmission line and transformer for any given generation or demand condition).
  5. PowerAnalytics.jl enables common analytic routines for power systems input and results data.
  6. PowerGraphics.jl supports interactive visualization of power systems input and results data.
  7. PowerNetworkMatrices.jl allows the performant calculation of large electric network matrices required to build power flow and contingency analyses.

The software suites included in Sienna are now freely available to the power systems research community. By addressing shortfalls of previous modeling platforms, Sienna helps move one step closer to breaking down barriers to the development and deployment of modern, renewable-based energy systems.

"The goal of Sienna is to create a common platform for electrical engineers to represent new technologies, computational scientists to develop algorithms, and analysts to conduct applied studies. Ultimately, we hope that Sienna will help advance the nation's ability to test and analyze our future grids," Barrows said. "This approach provides a helpful, accessible way to overcome the challenges in studying low-inertia systems, and we're excited to see these tools be applied to investigate a wide range of future renewable grid models."

Access the open-source Sienna ecosystem to learn more about the transformative, state-of-the art approach to comprehensive simulation and optimization of modern energy systems.

Tags: Energy Systems Integration,Energy Analysis,Grid Modernization,Computational Science